{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_topic_counts = pd.read_csv(\"topic_frequency.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_topic_frequency_by_day(df_topic_counts):\n",
    "    \n",
    "    df_topic_counts[\"topic\"] = df_topic_counts[\"topic\"].replace(\n",
    "        {\"wokeness and identity politics\": \"identity politics\"}\n",
    "    ).str.title()\n",
    "\n",
    "    \n",
    "    topic_day_counts = df_topic_counts.groupby(['day', 'topic'])['count'].sum().reset_index()\n",
    "\n",
    "    \n",
    "    pivot_table = topic_day_counts.pivot(index='day', columns='topic', values='count').fillna(0)\n",
    "\n",
    "    \n",
    "    color_mapping = {\n",
    "        'Democracy': '#87CEFA',  # Light Sky Blue\n",
    "        'Identity Politics': '#8A2BE2',  # Blue Violet\n",
    "        'Immigration': '#E75480',  # Dark Pink\n",
    "        'Inflation': '#FFA500',  # Orange\n",
    "        'Public Health Related Issues': '#FFFF66'  # Light Yellow\n",
    "    }\n",
    "\n",
    "    \n",
    "    pivot_table = pivot_table[color_mapping.keys()]\n",
    "\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(14, 6))\n",
    "\n",
    "    \n",
    "    pivot_table.plot(kind='bar', stacked=True, color=[color_mapping[col] for col in pivot_table.columns], ax=ax)\n",
    "\n",
    "    \n",
    "    for container in ax.containers:\n",
    "        labels = [f'{int(bar.get_height())}' if bar.get_height() > 0 else '' for bar in container]\n",
    "        ax.bar_label(container, labels=labels, label_type='center', fontsize=8, color='black')\n",
    "\n",
    "    \n",
    "    ax.set_xlabel('Day', fontsize=12)\n",
    "    ax.set_ylabel('Count of Comments', fontsize=12)\n",
    "\n",
    "    \n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.5)\n",
    "    ax.set_xticks(range(len(pivot_table.index)))\n",
    "    ax.set_xticklabels(pivot_table.index, rotation=0)\n",
    "\n",
    "    \n",
    "    ax.legend(title=\"Issue Area\", title_fontsize=11, bbox_to_anchor=(1.05, 1), loc=\"upper left\", frameon=True, fontsize=11)\n",
    "\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_topic_frequency_by_day(df_topic_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gk/xzdb9d351jnf4tlfsf3h79kc0000gn/T/ipykernel_98063/1550530293.py:14: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(x='count', y='topic', data=top_topics, palette='plasma')\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZpUYAAIobofs2c+HCBQ0cOFCurq7y8/PTrFmzCrTJycnR+PHjVa1aNVWoUEFt2rRRfHy8dXlMTIw8PT21adMm1a9fX66ururWrZvS0tIkXT7tesmSJVqzZo119Dk+Pt7m9PITJ06oU6dOkqSKFSvKYrFo8ODBWrp0qSpXrqycnBybmnr27KlHH330usfm6ekpX19fNWrUSAsXLtRvv/2m2NhYSZdPcWzdurWcnZ3l5+eniRMn6o8//rjmtv58enlgYKAkqVevXrJYLNbpq51e/t5776lhw4bW/URGRkqSDMNQVFSUAgIC5OzsrKpVq2rUqFHXPR4AuJXk5+drzJgxCg0NtZ7x9K9//UsVKlTQhAkTdPHiRV24cEHjx49XXl6e9T0FAIDSjtB9m3n66aeVkJCgNWvWaPPmzYqPj9e+ffts2kRGRmrHjh1avny5Dh06pL59+6pbt246evSotc3Fixf1+uuv6/3339eXX36p1NRUjR8/XtLl06779etnDeJpaWlq166dzT78/f21YsUKSVJKSorS0tI0d+5c9e3bV3l5efr888+tbTMyMrR+/XoNGTKk0Mfp4uIiSbp06ZJ++ukn3XPPPWrVqpUOHjyohQsXavHixXrppZcKta2kpCRJ/xtJvzL9VwsXLlRERISGDx+u5ORkff7556pTp44kacWKFZo9e7beeustHT16VKtXr1bjxo2vuc+cnBxlZWXZPACgNIuIiNDhw4e1fPly6zwvLy99+umnWrt2rVxdXeXh4aHMzEw1b95cZcrwEQUAcGsw5zxflEjZ2dlavHixPvjgA3Xp0kWStGTJElWvXt3aJjU1VdHR0UpNTVXVqlUlXQ7RGzduVHR0tKZPny5Jys3N1aJFi1S7dm1Jl4P6tGnTJF0+7drFxUU5OTny9fW9ai1ly5a1nl7o7e0tT09P67KHH35Y0dHR6tu3ryTpgw8+UEBAgDp27Fio47x48aImTZqksmXLqkOHDnrzzTfl7++v+fPny2KxKDg4WKdPn9aECRP0wgsv/O0HOy8vL0n/G0m/lpdeeklPPfWURo8ebZ3XqlUrSZefV19fX4WFhcnR0VEBAQFq3br1Nbc1Y8YMTZ06tVDHCwAlXWRkpNatW6cvv/zS5j1Hkrp27apjx47p559/loODg/X/2lq1atmpWgAAihdfI99Gjh07pkuXLqlNmzbWeZUqVVK9evWs08nJycrLy1PdunXl6upqfSQkJOjYsWPWduXLl7cGbkny8/NTRkZGsdQ5bNgwbd68WT/99JOky6ezDx48WBaL5brr9e/fX66urnJzc9OKFSu0ePFiNWnSREeOHFHbtm1t1g8NDVV2drZ+/PHHYqk5IyNDp0+ftn6Z8Vd9+/bVb7/9plq1amnYsGFatWrVdU9vf/bZZ3Xu3Dnr49SpU8VSJwDcTIZhKDIyUqtWrdLWrVtVs2bNa7atUqWKPD09tXXrVmVkZOi+++67iZUCAGAeRrphIzs7W2XLltXevXtVtmxZm2Wurq7Wvx0dHW2WWSwWGYZRLDU0a9ZMISEhWrp0qbp27aqvv/5a69ev/9v1Zs+erbCwMHl4eFhHp2+WK6ezX4u/v79SUlK0ZcsWxcbGasSIEXrttdeUkJBQ4LmUJGdnZ5sbCwFAaRQREaFly5ZpzZo1cnNzU3p6uiTJw8PD+v9mdHS06tevLy8vL+3YsUOjR4/W2LFjbb4QBgCgNCN030Zq164tR0dH7dq1SwEBAZKkX3/9Vd999506dOgg6XLgzcvLU0ZGhu68884b3peTk5Py8vL+to2kq7YbOnSo5syZo59++klhYWHy9/f/2336+vpar6H+s/r162vFihUyDMM62p2YmCg3N7cCpzlei6Oj43WPx83NTYGBgYqLi7PeIO6vXFxc1KNHD/Xo0UMREREKDg5WcnKymjdvXqgaAKC0WbhwoSQVuDwoOjpagwcPlnT5vh7PPvuszp49q8DAQD3//PMaO3bsTa4UAADzELpvI66urnr88cf19NNPq3LlyvL29tbzzz9vc01z3bp1NWDAAA0cOFCzZs1Ss2bN9N///ldxcXFq0qSJ7r333kLtKzAwUJs2bVJKSooqV64sDw+PAm1q1Kghi8WidevW6Z577pGLi4t1NP3hhx/W+PHj9c4772jp0qX/6LhHjBihOXPmaOTIkYqMjFRKSoqmTJmicePGFfpGPVcCdWhoqJydnVWxYsUCbaKiovTEE0/I29tbd999t86fP6/ExESNHDlSMTExysvLU5s2bVS+fHl98MEHcnFxUY0aNf7RsQFASVaYM6BmzpypmTNn3oRqAACwD67pvs289tpruvPOO9WjRw+FhYXpjjvuUIsWLWzaREdHa+DAgXrqqadUr1499ezZU0lJSdbR8cIYNmyY6tWrp5YtW8rLy0uJiYkF2lSrVk1Tp07VxIkT5ePjY/15LenyqYd9+vSRq6urevbsecPHe2U/X3zxhXbv3q2QkBA98cQTevzxxzVp0qRCb2PWrFmKjY2Vv7+/mjVrdtU2gwYN0pw5c/Tmm2+qYcOG6t69u/WO756ennrnnXcUGhqqJk2aaMuWLVq7di2/8w0AAADc4ixGcV2ICxSzLl26qGHDhpo3b569S7G7rKwseXh46MWgl1WubDl7lwMAkqTxR8bZuwQAAOzmymf0c+fOyd3d/ZrtOL0cJc6vv/6q+Ph4xcfH680337R3OQAAAABwwwjdKHGaNWumX3/9Va+88gp3rwUAAABQqhG6UeKcOHHC3iUAAAAAQLHgRmoAAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJHOxdAIDCG7UnUu7u7vYuAwAAAEAhMdINAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmMThRlbas2ePPvnkE6WmpurSpUs2y1auXFkshQEoaNuQl1TB0dneZQC3hLCPXrR3CQAA4DZQ5JHu5cuXq127djpy5IhWrVql3Nxcff3119q6das8PDzMqBEAAAAAgFKpyKF7+vTpmj17ttauXSsnJyfNnTtX3377rfr166eAgAAzagQAAAAAoFQqcug+duyY7r33XkmSk5OTLly4IIvForFjx+rtt98u9gIBAAAAACitihy6K1asqPPnz0uSqlWrpsOHD0uSMjMzdfHixeKtDgAAAACAUqzIN1Jr3769YmNj1bhxY/Xt21ejR4/W1q1bFRsbqy5duphRIwAAAAAApVKRQ/f8+fP1+++/S5Kef/55OTo6avv27erTp48mTZpU7AUCAAAAAFBaFTl0V6pUyfp3mTJlNHHixGItCAAAAACAW0WRr+mWLt9MbdKkSerfv78yMjIkSRs2bNDXX39drMUBAAAAAFCaFTl0JyQkqHHjxtq1a5dWrlyp7OxsSdLBgwc1ZcqUYi8QAAAAAIDSqsihe+LEiXrppZcUGxsrJycn6/zOnTtr586dxVocAAAAAAClWZFDd3Jysnr16lVgvre3t37++ediKQoAAAAAgFtBkUO3p6en0tLSCszfv3+/qlWrVixFAQAAAABwKyhy6H7ooYc0YcIEpaeny2KxKD8/X4mJiRo/frwGDhxoRo0AAAAAAJRKRQ7d06dPV3BwsPz9/ZWdna0GDRqoffv2ateuHb/TDQAAAADAnxTpd7oNw1B6errmzZunF154QcnJycrOzlazZs0UFBRkVo0AAAAAAJRKRQ7dderU0ddff62goCD5+/ubVRcAAAAAAKVekU4vL1OmjIKCgvTLL7+YVQ8AAAAAALeMIl/TPXPmTD399NM6fPiwGfUAAAAAAHDLKNLp5ZI0cOBAXbx4USEhIXJycpKLi4vN8rNnzxZbcQAAAAAAlGZFDt1z5swxoQwAAAAAAG49RQ7dgwYNuuYyRrkBAAAAAPifIl/TfTWbN29Wv379VK1ateLYHAAAAAAAt4QbDt0nT57UlClTFBgYqL59+6pMmTJaunRpcdYGAAAAAECpVqTTyy9duqSVK1fq3XffVWJiosLCwvTjjz9q//79aty4sVk1AgAAAABQKhV6pHvkyJGqWrWq5s6dq169eunHH3/U2rVrZbFYVLZsWTNrBAAAAACgVCr0SPfChQs1YcIETZw4UW5ubmbWBAAAAADALaHQI93vv/++du/eLT8/Pz344INat26d8vLyzKwNAAAAAIBSrdChu3///oqNjVVycrKCg4MVEREhX19f5efn65tvvjGzRgAAbroZM2aoVatWcnNzk7e3t3r27KmUlBSbNm+//bY6duwod3d3WSwWZWZm2qdYAABQYhX57uU1a9bU1KlTdeLECX3wwQfq06ePHnnkEVWvXl2jRo0yo0YAAG66hIQERUREaOfOnYqNjVVubq66du2qCxcuWNtcvHhR3bp103PPPWfHSgEAQElWpLuX/5nFYlF4eLjCw8N19uxZLV26VNHR0cVZG0qZwYMHa8mSJZIkBwcHVapUSU2aNFH//v01ePBglSlTLD8LDwA3xcaNG22mY2Ji5O3trb1796p9+/aSpDFjxkiS4uPjb3J1AACgtCiWFFSpUiWNGTNGBw8eLI7NoRTr1q2b0tLSdOLECW3YsEGdOnXS6NGj1b17d/3xxx/2Lq9ILl26ZO8SAJQg586dk3T5PQ8AAKCwGHpEsXJ2dpavr6+qVaum5s2b67nnntOaNWu0YcMGxcTESJIyMzM1dOhQeXl5yd3dXZ07d7b5wiYqKkpNmzbVe++9p4CAALm6umrEiBHKy8vTq6++Kl9fX3l7e+vll1+22Xdqaqruv/9+ubq6yt3dXf369dOZM2ds2qxdu1atWrVSuXLlVKVKFfXq1cu6LDAwUC+++KIGDhwod3d3DR8+XJI0YcIE1a1bV+XLl1etWrU0efJk5ebmFmq706ZNU6NGjQo8T02bNtXkyZNv/IkGcFPl5+drzJgxCg0NveprGgAA4FoI3TBd586dFRISopUrV0qS+vbtq4yMDG3YsEF79+5V8+bN1aVLF509e9a6zrFjx7RhwwZt3LhRH330kRYvXqx7771XP/74oxISEvTKK69o0qRJ2rVrl6TLH4jvv/9+nT17VgkJCYqNjdUPP/ygBx980LrN9evXq1evXrrnnnu0f/9+xcXFqXXr1ja1vv766woJCdH+/futodjNzU0xMTH65ptvNHfuXL3zzjuaPXt2obY7ZMgQHTlyRElJSdb2+/fv16FDh/TYY49d8znLyclRVlaWzQOA/UREROjw4cNavny5vUsBAAClzA1f0w0URXBwsA4dOqSvvvpKu3fvVkZGhpydnSVdDrqrV6/WZ599Zh1dzs/P13vvvSc3Nzc1aNBAnTp1UkpKir744guVKVNG9erV0yuvvKJt27apTZs2iouLU3Jyso4fPy5/f39J0tKlS9WwYUMlJSWpVatWevnll/XQQw9p6tSp1rpCQkJs6uzcubOeeuopm3mTJk2y/h0YGKjx48dr+fLleuaZZyTpututXr26wsPDFR0drVatWkmSoqOj1aFDB9WqVeuaz9eMGTNstgfAfiIjI7Vu3Tp9+eWXql69ur3LAQAApQwj3bgpDMOQxWLRwYMHlZ2drcqVK8vV1dX6OH78uI4dO2ZtHxgYKDc3N+u0j4+PGjRoYHMzNh8fH2VkZEiSjhw5In9/f2vglqQGDRrI09NTR44ckSQdOHBAXbp0uW6dLVu2LDDv448/VmhoqHx9feXq6qpJkyYpNTXVuvzvtjts2DB99NFH+v3333Xp0iUtW7ZMQ4YMuW4dzz77rM6dO2d9nDp16rrtARQ/wzAUGRmpVatWaevWrapZs6a9SwIAAKXQDY10/+c//9Fbb72lY8eO6bPPPlO1atX0/vvvq2bNmrrjjjuKu0bcAo4cOaKaNWsqOztbfn5+V73Tr6enp/VvR0dHm2UWi+Wq8/Lz8wtdg4uLy9+2qVChgs30jh07NGDAAE2dOlXh4eHy8PDQ8uXLNWvWrEJvt0ePHnJ2dtaqVavk5OSk3NxcPfDAA9ddx9nZ2XomAAD7iIiI0LJly7RmzRq5ubkpPT1dkuTh4WF93aenpys9PV3ff/+9JCk5OVlubm4KCAjghmsAAEDSDYx0r1ixQuHh4XJxcdH+/fuVk5Mj6fJdXadPn17sBaL027p1q5KTk9WnTx81b95c6enpcnBwUJ06dWweVapUueF91K9fX6dOnbIZEf7mm2+UmZmpBg0aSJKaNGmiuLi4Im13+/btqlGjhp5//nm1bNlSQUFBOnnypE2bv9uug4ODBg0apOjoaEVHR+uhhx4q1BcAAOxr4cKFOnfunDp27Cg/Pz/r4+OPP7a2WbRokZo1a6Zhw4ZJktq3b69mzZrp888/t1fZAACghCnySPdLL72kRYsWaeDAgTY3lAkNDdVLL71UrMWh9MnJyVF6erry8vJ05swZbdy4UTNmzFD37t01cOBAlSlTRm3btlXPnj316quvqm7dujp9+rT1ZmRXO727MMLCwtS4cWMNGDBAc+bM0R9//KERI0aoQ4cO1m1OmTJFXbp0Ue3atfXQQw/pjz/+0BdffKEJEyZcc7tBQUFKTU3V8uXL1apVK61fv16rVq2yaVOY7Q4dOlT169eXJCUmJt7QMQK4uQzD+Ns2UVFRioqKMr8YAABQahV5pDslJUXt27cvMN/Dw0OZmZnFURNKsY0bN8rPz0+BgYHq1q2btm3bpnnz5mnNmjUqW7asLBaLvvjiC7Vv316PPfaY6tatq4ceekgnT56Uj4/PDe/XYrFozZo1qlixotq3b6+wsDDVqlXLZkSqY8eO+vTTT/X555+radOm6ty5s3bv3n3d7d53330aO3asIiMj1bRpU23fvr3AT30VZrtBQUFq166dgoOD1aZNmxs+TgAAAACli8UozFf5f1KrVi29/fbbCgsLk5ubmw4ePKhatWpp6dKlmjlzpr755huzagVKLcMwFBQUpBEjRmjcuHFFXj8rK0seHh5a3edpVXDkWm+gOIR99KK9SwAAAKXYlc/o586dk7u7+zXbFXmke9iwYRo9erR27doli8Wi06dP68MPP9T48eP15JNP/qOigVvRf//7X82fP1/p6enX/W1uAAAAALeeIl/TPXHiROXn56tLly66ePGi2rdvL2dnZ40fP14jR440o0agVPP29laVKlX09ttvq2LFivYuBwAAAMBNVOTQbbFY9Pzzz+vpp5/W999/r+zsbDVo0ECurq5m1AeUekW8ggMAAADALeSGfqdbkpycnKw/xQQAAAAAAAoqcuju1KmTLBbLNZdv3br1HxUEAAAAAMCtosihu2nTpjbTubm5OnDggA4fPqxBgwYVV10AAAAAAJR6RQ7ds2fPvur8qKgoZWdn/+OCAAAAAAC4VRT5J8Ou5ZFHHtF7771XXJsDAAAAAKDUK7bQvWPHDpUrV664NgcAAAAAQKlX5NPLe/fubTNtGIbS0tK0Z88eTZ48udgKAwAAAACgtCty6Pbw8LCZLlOmjOrVq6dp06apa9euxVYYAAAAAAClXZFDd3R0tBl1AAAAAABwyynyNd2nTp3Sjz/+aJ3evXu3xowZo7fffrtYCwMAAAAAoLQrcuh++OGHtW3bNklSenq6wsLCtHv3bj3//POaNm1asRcIAAAAAEBpVeTQffjwYbVu3VqS9Mknn6hx48bavn27PvzwQ8XExBR3fQAAAAAAlFpFDt25ublydnaWJG3ZskX33XefJCk4OFhpaWnFWx0AAAAAAKVYkUN3w4YNtWjRIv3nP/9RbGysunXrJkk6ffq0KleuXOwFAgAAAABQWhU5dL/yyit666231LFjR/Xv318hISGSpM8//9x62jkAAAAAALiBnwzr2LGjfv75Z2VlZalixYrW+cOHD1f58uWLtTgAAAAAAEqzIo90//bbb8rJybEG7pMnT2rOnDlKSUmRt7d3sRcIAAAAAEBpVeTQff/992vp0qWSpMzMTLVp00azZs1Sz549tXDhwmIvEAAAAACA0qrIoXvfvn268847JUmfffaZfHx8dPLkSS1dulTz5s0r9gIBAAAAACitihy6L168KDc3N0nS5s2b1bt3b5UpU0b/+te/dPLkyWIvEAAAAACA0qrIobtOnTpavXq1Tp06pU2bNqlr166SpIyMDLm7uxd7gQAAAAAAlFZFDt0vvPCCxo8fr8DAQLVp00Zt27aVdHnUu1mzZsVeIAAAAAAApVWRfzLsgQce0B133KG0tDTrb3RLUpcuXdSrV69iLQ4AAAAAgNKsyKFbknx9feXr62szr3Xr1sVSEAAAAAAAt4pCh+7evXsXqt3KlStvuBgA19fpvUncOwEAAAAoRQoduj08PMysAwAAAACAW06hQ3d0dLSZdQAAAAAAcMsp8t3LAQAAAABA4RC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTONi7AACFd+yV0XIt52TvMoASK2jyW/YuAQAAwAYj3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYpNSH7sDAQM2ZM+e6bSwWi1avXi1JOnHihCwWiw4cOGB6bcWhY8eOGjNmzHXbxMTEyNPT86bU81eFqc8s8fHxslgsyszMtMv+AZROM2bMUKtWreTm5iZvb2/17NlTKSkpNm1+//13RUREqHLlynJ1dVWfPn105swZO1UMAABKM7uG7sGDB8tischiscjJyUl16tTRtGnT9Mcff5i2T39/f6WlpalRo0Y3tP71QvvNCKCF+ZKhMK4cx5VHpUqV1KFDB/3nP//550Vex80OysX1fAG4dSQkJCgiIkI7d+5UbGyscnNz1bVrV124cMHaZuzYsVq7dq0+/fRTJSQk6PTp0+rdu7cdqwYAAKWVg70L6Natm6Kjo5WTk6MvvvhCERERcnR01LPPPmvK/sqWLStfX19Ttl0abdmyRQ0bNtTPP/+sl19+Wd27d9d3330nHx8fe5cGAKbYuHGjzXRMTIy8vb21d+9etW/fXufOndPixYu1bNkyde7cWZIUHR2t+vXra+fOnfrXv/5lj7IBAEApZffTy52dneXr66saNWroySefVFhYmD7//HNJVx857tmzpwYPHmwz7/z58+rfv78qVKigatWqacGCBdfc39VGqr/++mt1795d7u7ucnNz05133qljx47942PLycnR+PHjVa1aNVWoUEFt2rRRfHy8dfkvv/yi/v37q1q1aipfvrwaN26sjz766Jrb69ixo06ePKmxY8daR6j/bNOmTapfv75cXV3VrVs3paWl/W2NlStXlq+vrxo1aqTnnntOWVlZ2rVrl3X54cOHdffdd8vV1VU+Pj569NFH9fPPP19ze++//75atmwpNzc3+fr66uGHH1ZGRoaky899p06dJEkVK1aUxWKx9mV+fr5mzJihmjVrysXFRSEhIfrss89stv3FF1+obt26cnFxUadOnXTixIm/Pb4/MwxDUVFRCggIkLOzs6pWrapRo0ZZl7/55psKCgpSuXLl5OPjowceeMC67Goj5k2bNlVUVJR1OjMzU0OHDpWXl5fc3d3VuXNnHTx40Lr84MGD6tSpk9zc3OTu7q4WLVpoz549RToGAMXv3LlzkqRKlSpJkvbu3avc3FyFhYVZ2wQHBysgIEA7duywS40AAKD0snvo/isXFxddunSpSOu89tprCgkJ0f79+zVx4kSNHj1asbGxhVr3p59+Uvv27eXs7KytW7dq7969GjJkSLGc4h4ZGakdO3Zo+fLlOnTokPr27atu3brp6NGjki5fM9iiRQutX79ehw8f1vDhw/Xoo49q9+7dV93eypUrVb16dU2bNk1paWk2ofrixYt6/fXX9f777+vLL79Uamqqxo8fX+haf/vtNy1dulSS5OTkJOlyiOzcubOaNWumPXv2aOPGjTpz5oz69et3ze3k5ubqxRdf1MGDB7V69WqdOHHCGqz9/f21YsUKSVJKSorS0tI0d+5cSZevsVy6dKkWLVqkr7/+WmPHjtUjjzyihIQESdKpU6fUu3dv9ejRQwcOHNDQoUM1ceLEQh+fJK1YsUKzZ8/WW2+9paNHj2r16tVq3LixJGnPnj0aNWqUpk2bppSUFG3cuFHt27cv0vb79u2rjIwMbdiwQXv37lXz5s3VpUsXnT17VpI0YMAAVa9eXUlJSdq7d68mTpwoR0fHIu0DQPHKz8/XmDFjFBoaar3sKD09XU5OTgXuleHj46P09HQ7VAkAAEozu59efoVhGIqLi9OmTZs0cuTIIq0bGhpqDWB169ZVYmKiZs+erbvuuutv112wYIE8PDy0fPlyawCqW7fu367Xrl07lSlj+53Fb7/9pqZNm0qSUlNTFR0drdTUVFWtWlWSNH78eG3cuFHR0dGaPn26qlWrZhOMR44cqU2bNumTTz5R69atC+yzUqVKKlu2rHUU+c9yc3O1aNEi1a5dW9LlwD9t2rRCH8fFixdlGIZatGihLl26SJLmz5+vZs2aafr06db27733nvz9/fXdd99d9XkaMmSI9e9atWpp3rx5atWqlbKzs+Xq6modSfL29rZ+oM3JydH06dO1ZcsWtW3b1rruV199pbfeeksdOnTQwoULVbt2bc2aNUuSVK9ePSUnJ+uVV17522O8IjU1Vb6+vgoLC5Ojo6MCAgKsz3NqaqoqVKig7t27y83NTTVq1FCzZs0Kve2vvvpKu3fvVkZGhpydnSVJr7/+ulavXq3PPvtMw4cPV2pqqp5++mkFBwdLkoKCgq65vZycHOXk5Fins7KyCl0LgMKLiIjQ4cOH9dVXX9m7FAAAcIuye+het26dXF1dlZubq/z8fD388MM2p+wWxpWg9ufpwt4868CBA7rzzjuLPOL48ccfq379+jbzBgwYYP07OTlZeXl5BYJpTk6OKleuLEnKy8vT9OnT9cknn+inn37SpUuXlJOTo/LlyxepFkkqX768NXBLkp+fn/W07r87juDgYB0+fFjPPPOMYmJirM/FwYMHtW3bNrm6uhZY79ixY1cN3Xv37lVUVJQOHjyoX3/9Vfn5+ZIuh9oGDRpctYbvv/9eFy9eLPAlyaVLl6zB98iRI2rTpo3N8r/2+9/p27ev5syZo1q1aqlbt26655571KNHDzk4OOiuu+5SjRo1rMu6deumXr16FbovDh48qOzsbGvfXvHbb79ZL1UYN26chg4dqvfff19hYWHq27evTZ/92YwZMzR16tQiHR+AoomMjNS6dev05Zdfqnr16tb5vr6+unTpkjIzM21Gu8+cOcM9QQAAQJHZPXR36tRJCxculJOTk6pWrSoHh/+VVKZMGRmGYdM+Nze3WPfv4uJyQ+v5+/urTp0619xWdna2ypYtq71796ps2bI27a6E2Ndee01z587VnDlz1LhxY1WoUEFjxowp8un1kgp8aWCxWAo8d9c6jqCgIAUFBemPP/5Qr169dPjwYTk7Oys7O1s9evS46miyn59fgXkXLlxQeHi4wsPD9eGHH8rLy0upqakKDw+/7jFlZ2dLktavX69q1arZLLsyalwc/P39lZKSoi1btig2NlYjRozQa6+9poSEBLm5uWnfvn2Kj4/X5s2b9cILLygqKkpJSUny9PT823+L2dnZ8vPzs7lm/4orH9qjoqL08MMPa/369dqwYYOmTJmi5cuXq1evXgXWefbZZzVu3DjrdFZWlvz9/YvniQBuc4ZhaOTIkVq1apXi4+NVs2ZNm+UtWrSQo6Oj4uLi1KdPH0mXL4lJTU0t8pd9AAAAdg/dFSpUKBBer/Dy8rK5bjkvL0+HDx+23ozrip07dxaY/uso9LU0adJES5YsUW5ubrFeX9usWTPl5eUpIyNDd95551XbJCYm6v7779cjjzwi6fK1hd999901R4Sly9db5+XlFVudf/bAAw/ohRde0JtvvqmxY8eqefPmWrFihQIDA22+DLmWb7/9Vr/88otmzpxpDYh/vVHYlevF/3wMDRo0kLOzs1JTU9WhQ4erbrt+/frWG+xd8dd+LwwXFxf16NFDPXr0UEREhIKDg5WcnKzmzZvLwcFBYWFhCgsL05QpU+Tp6amtW7eqd+/eBf4tZmVl6fjx49bp5s2bKz09XQ4ODgoMDLzm/uvWrau6detq7Nix6t+/v6Kjo68aup2dnYv1CwcA/xMREaFly5ZpzZo1cnNzs16n7eHhIRcXF3l4eOjxxx/XuHHjVKlSJbm7u2vkyJFq27Ytdy4HAABFVuJupPZnnTt31vr167V+/Xp9++23evLJJ6/6+86JiYl69dVX9d1332nBggX69NNPNXr06ELtIzIyUllZWXrooYe0Z88eHT16VO+//75SUlL+Ue1169bVgAEDNHDgQK1cuVLHjx/X7t27NWPGDK1fv17S5Wt6Y2NjtX37dh05ckT/93//pzNnzlx3u4GBgfryyy/1008/Xfcu4jfCYrFo1KhRmjlzpi5evKiIiAidPXtW/fv3V1JSko4dO6ZNmzbpscceu2rwDwgIkJOTk9544w398MMP+vzzz/Xiiy/atKlRo4YsFovWrVun//73v8rOzpabm5vGjx+vsWPHasmSJTp27Jj27dunN954Q0uWLJEkPfHEEzp69KiefvpppaSkaNmyZYqJiSnS8cXExGjx4sU6fPiwfvjhB33wwQdycXFRjRo1tG7dOs2bN08HDhzQyZMntXTpUuXn56tevXqSLv9bfP/99/Wf//xHycnJGjRokM0ZDGFhYWrbtq169uypzZs368SJE9q+fbuef/557dmzR7/99psiIyMVHx+vkydPKjExUUlJSYX+cghA8Vm4cKHOnTunjh07ys/Pz/r4+OOPrW1mz56t7t27q0+fPmrfvr18fX21cuVKO1YNAABKqxIduocMGaJBgwZp4MCB6tChg2rVqlVglFuSnnrqKe3Zs0fNmjXTSy+9pH//+98KDw8v1D4qV66srVu3Kjs7Wx06dFCLFi30zjvvFMuod3R0tAYOHKinnnpK9erVU8+ePZWUlKSAgABJ0qRJk9S8eXOFh4erY8eO8vX1Vc+ePa+7zWnTpunEiROqXbu2vLy8/nGNfzVo0CDl5uZq/vz5qlq1qhITE5WXl6euXbuqcePGGjNmjPV067/y8vJSTEyMPv30UzVo0EAzZ87U66+/btOmWrVqmjp1qiZOnCgfHx9FRkZKkl588UVNnjxZM2bMUP369dWtWzetX7/eetpnQECAVqxYodWrVyskJESLFi2yucFbYXh6euqdd95RaGiomjRpoi1btmjt2rWqXLmyPD09tXLlSnXu3Fn169fXokWL9NFHH6lhw4aSLp/u3aFDB3Xv3l333nuvevbsaXM9tsVi0RdffKH27dvrscceU926dfXQQw/p5MmT8vHxUdmyZfXLL79o4MCBqlu3rvr166e7776b67YBOzAM46qPP/8cZbly5bRgwQKdPXtWFy5c0MqVK7meGwAA3BCLUZgLfwHYVVZWljw8PLTvucFyLedk73KAEito8lv2LgEAANwmrnxGP3funNzd3a/ZrkSPdAMAAAAAUJoRugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwiYO9CwBQeLUnzJW7u7u9ywAAAABQSIx0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACZxsHcBAArv9Jo+Ol/e0d5l4DZWrc8X9i4BAACgVGGkGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAMXqyy+/VI8ePVS1alVZLBatXr26QJsjR47ovvvuk4eHhypUqKBWrVopNTX15hcLAABgMkI3bjnp6em66667VKFCBXl6ekrSNT/4F1VxbQe4lV24cEEhISFasGDBVZcfO3ZMd9xxh4KDgxUfH69Dhw5p8uTJKleu3E2uFAAAwHwO9i4A+DuDBw9WZmZmocPu7NmzlZaWpgMHDsjDw+OG9hkVFaXVq1frwIEDNvPT0tJUsWLFG9omcLu4++67dffdd19z+fPPP6977rlHr776qnVe7dq1b0ZpAAAANx0j3bjlHDt2TC1atFBQUJC8vb2Lddu+vr5ydnYu1m0Ct5P8/HytX79edevWVXh4uLy9vdWmTRvOIAEAALcsQjdKlY4dO2rUqFF65plnVKlSJfn6+ioqKsq6PDAwUCtWrNDSpUtlsVg0ePDgq25nwoQJqlu3rsqXL69atWpp8uTJys3NlSTFxMRo6tSpOnjwoCwWiywWi2JiYiQVPL08OTlZnTt3louLiypXrqzhw4crOzvbunzw4MHq2bOnXn/9dfn5+aly5cqKiIiw7gu43WRkZCg7O1szZ85Ut27dtHnzZvXq1Uu9e/dWQkKCvcsDAAAodpxejlJnyZIlGjdunHbt2qUdO3Zo8ODBCg0N1V133aWkpCQNHDhQ7u7umjt3rlxcXK66DTc3N8XExKhq1apKTk7WsGHD5ObmpmeeeUYPPvigDh8+rI0bN2rLli2SdNXT1C9cuKDw8HC1bdtWSUlJysjI0NChQxUZGWkN6ZK0bds2+fn5adu2bfr+++/14IMPqmnTpho2bNg1jzEnJ0c5OTnW6aysrBt8toCSJT8/X5J0//33a+zYsZKkpk2bavv27Vq0aJE6dOhgz/IAAACKHSPdKHWaNGmiKVOmKCgoSAMHDlTLli0VFxcnSfLy8pKzs7NcXFzk6+t7zWu6J02apHbt2ikwMFA9evTQ+PHj9cknn0iSXFxc5OrqKgcHB/n6+srX1/eq4X3ZsmX6/ffftXTpUjVq1EidO3fW/Pnz9f777+vMmTPWdhUrVtT8+fMVHBys7t27695777XWey0zZsyQh4eH9eHv73+jTxdQolSpUkUODg5q0KCBzfz69etz93IAAHBLInSj1GnSpInNtJ+fnzIyMoq0jY8//lihoaHy9fWVq6urJk2aVOQP/EeOHFFISIgqVKhgnRcaGqr8/HylpKRY5zVs2FBly5YtUr3PPvuszp07Z32cOnWqSLUBJZWTk5NatWpl8xqRpO+++041atSwU1UAAADm4fRylDqOjo420xaLxXrKamHs2LFDAwYM0NSpUxUeHi4PDw8tX75cs2bNKu5SJd1Yvc7OztywDaVWdna2vv/+e+v08ePHdeDAAVWqVEkBAQF6+umn9eCDD6p9+/bq1KmTNm7cqLVr1yo+Pt5+RQMAAJiE0I3bzvbt21WjRg09//zz1nknT560aePk5KS8vLzrbqd+/fqKiYnRhQsXrKPdiYmJKlOmjOrVq1f8hQOlxJ49e9SpUyfr9Lhx4yRJgwYNUkxMjHr16qVFixZpxowZGjVqlOrVq6cVK1bojjvusFfJAAAApiF047YTFBSk1NRULV++XK1atdL69eu1atUqmzaBgYHW0bnq1avLzc2twMjzgAEDNGXKFA0aNEhRUVH673//q5EjR+rRRx+Vj4/PzTwkoETp2LGjDMO4bpshQ4ZoyJAhN6kiAAAA++Gabtx27rvvPo0dO1aRkZHWuyZPnjzZpk2fPn3UrVs3derUSV5eXvroo48KbKd8+fLatGmTzp49q1atWumBBx5Qly5dNH/+/Jt1KAAAAABKOIvxd8MRAOwuKytLHh4eOrI0TG7lHf9+BcAk1fp8Ye8SAAAASoQrn9HPnTsnd3f3a7ZjpBsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkzjYuwAAhVf1/hVyd3e3dxkAAAAAComRbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATMLvdAOlgGEYkqSsrCw7VwIAAABA+t9n8yuf1a+F0A2UAr/88oskyd/f386VAAAAAPiz8+fPy8PD45rLCd1AKVCpUiVJUmpq6nVf0Lh5srKy5O/vr1OnTsnd3d3e5UD0SUlFv5Q89EnJRL+UPPRJyVSS+sUwDJ0/f15Vq1a9bjtCN1AKlClz+fYLHh4edv/PBbbc3d3pkxKGPimZ6JeShz4pmeiXkoc+KZlKSr8UZkCMG6kBAAAAAGASQjcAAAAAACYhdAOlgLOzs6ZMmSJnZ2d7l4L/jz4peeiTkol+KXnok5KJfil56JOSqTT2i8X4u/ubAwAAAACAG8JINwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDJdyCBQsUGBiocuXKqU2bNtq9e7e9S7qtfPnll+rRo4eqVq0qi8Wi1atX2yw3DEMvvPCC/Pz85OLiorCwMB09etQ+xd4mZsyYoVatWsnNzU3e3t7q2bOnUlJSbNr8/vvvioiIUOXKleXq6qo+ffrozJkzdqr41rdw4UI1adLE+pupbdu21YYNG6zL6Q/7mzlzpiwWi8aMGWOdR7/cfFFRUbJYLDaP4OBg63L6xD5++uknPfLII6pcubJcXFzUuHFj7dmzx7qc9/qbLzAwsMBrxWKxKCIiQlLpe60QuoES7OOPP9a4ceM0ZcoU7du3TyEhIQoPD1dGRoa9S7ttXLhwQSEhIVqwYMFVl7/66quaN2+eFi1apF27dqlChQoKDw/X77//fpMrvX0kJCQoIiJCO3fuVGxsrHJzc9W1a1dduHDB2mbs2LFau3atPv30UyUkJOj06dPq3bu3Hau+tVWvXl0zZ87U3r17tWfPHnXu3Fn333+/vv76a0n0h70lJSXprbfeUpMmTWzm0y/20bBhQ6WlpVkfX331lXUZfXLz/frrrwoNDZWjo6M2bNigb775RrNmzVLFihWtbXivv/mSkpJsXiexsbGSpL59+0oqha8VA0CJ1bp1ayMiIsI6nZeXZ1StWtWYMWOGHau6fUkyVq1aZZ3Oz883fH19jddee806LzMz03B2djY++ugjO1R4e8rIyDAkGQkJCYZhXO4DR0dH49NPP7W2OXLkiCHJ2LFjh73KvO1UrFjRePfdd+kPOzt//rwRFBRkxMbGGh06dDBGjx5tGAavE3uZMmWKERISctVl9Il9TJgwwbjjjjuuuZz3+pJh9OjRRu3atY38/PxS+VphpBsooS5duqS9e/cqLCzMOq9MmTIKCwvTjh077FgZrjh+/LjS09Nt+sjDw0Nt2rShj26ic+fOSZIqVaokSdq7d69yc3Nt+iU4OFgBAQH0y02Ql5en5cuX68KFC2rbti39YWcRERG69957bZ5/ideJPR09elRVq1ZVrVq1NGDAAKWmpkqiT+zl888/V8uWLdW3b195e3urWbNmeuedd6zLea+3v0uXLumDDz7QkCFDZLFYSuVrhdANlFA///yz8vLy5OPjYzPfx8dH6enpdqoKf3alH+gj+8nPz9eYMWMUGhqqRo0aSbrcL05OTvL09LRpS7+YKzk5Wa6urnJ2dtYTTzyhVatWqUGDBvSHHS1fvlz79u3TjBkzCiyjX+yjTZs2iomJ0caNG7Vw4UIdP35cd955p86fP0+f2MkPP/yghQsXKigoSJs2bdKTTz6pUaNGacmSJZJ4ry8JVq9erczMTA0ePFhS6fz/y8HeBQAAcKMiIiJ0+PBhm2siYR/16tXTgQMHdO7cOX322WcaNGiQEhIS7F3WbevUqVMaPXq0YmNjVa5cOXuXg//v7rvvtv7dpEkTtWnTRjVq1NAnn3wiFxcXO1Z2+8rPz1fLli01ffp0SVKzZs10+PBhLVq0SIMGDbJzdZCkxYsX6+6771bVqlXtXcoNY6QbKKGqVKmismXLFrgT45kzZ+Tr62unqvBnV/qBPrKPyMhIrVu3Ttu2bVP16tWt8319fXXp0iVlZmbatKdfzOXk5KQ6deqoRYsWmjFjhkJCQjR37lz6w0727t2rjIwMNW/eXA4ODnJwcFBCQoLmzZsnBwcH+fj40C8lgKenp+rWravvv/+e14qd+Pn5qUGDBjbz6tevbz3tn/d6+zp58qS2bNmioUOHWueVxtcKoRsooZycnNSiRQvFxcVZ5+Xn5ysuLk5t27a1Y2W4ombNmvL19bXpo6ysLO3atYs+MpFhGIqMjNSqVau0detW1axZ02Z5ixYt5OjoaNMvKSkpSk1NpV9uovz8fOXk5NAfdtKlSxclJyfrwIED1kfLli01YMAA69/0i/1lZ2fr2LFj8vPz47ViJ6GhoQV+dvK7775TjRo1JPFeb2/R0dHy9vbWvffea51XKl8r9r6TG4BrW758ueHs7GzExMQY33zzjTF8+HDD09PTSE9Pt3dpt43z588b+/fvN/bv329IMv79738b+/fvN06ePGkYhmHMnDnT8PT0NNasWWMcOnTIuP/++42aNWsav/32m50rv3U9+eSThoeHhxEfH2+kpaVZHxcvXrS2eeKJJ4yAgABj69atxp49e4y2bdsabdu2tWPVt7aJEycaCQkJxvHjx41Dhw4ZEydONCwWi7F582bDMOiPkuLPdy83DPrFHp566ikjPj7eOH78uJGYmGiEhYUZVapUMTIyMgzDoE/sYffu3YaDg4Px8ssvG0ePHjU+/PBDo3z58sYHH3xgbcN7vX3k5eUZAQEBxoQJEwosK22vFUI3UMK98cYbRkBAgOHk5GS0bt3a2Llzp71Luq1s27bNkFTgMWjQIMMwLv+UyOTJkw0fHx/D2dnZ6NKli5GSkmLfom9xV+sPSUZ0dLS1zW+//WaMGDHCqFixolG+fHmjV69eRlpamv2KvsUNGTLEqFGjhuHk5GR4eXkZXbp0sQZuw6A/Soq/hm765eZ78MEHDT8/P8PJycmoVq2a8eCDDxrff/+9dTl9Yh9r1641GjVqZDg7OxvBwcHG22+/bbOc93r72LRpkyHpqs91aXutWAzDMOwyxA4AAAAAwC2Oa7oBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAoBDS09M1cuRI1apVS87OzvL391ePHj0UFxd3U+uwWCxavXr1Td0nAODGOdi7AAAAgJLuxIkTCg0Nlaenp1577TU1btxYubm52rRpkyIiIvTtt9/au0QAQAllMQzDsHcRAAAAJdk999yjQ4cOKSUlRRUqVLBZlpmZKU9PT6WmpmrkyJGKi4tTmTJl1K1bN73xxhvy8fGRJA0ePFiZmZk2o9RjxozRgQMHFB8fL0nq2LGjmjRponLlyundd9+Vk5OTnnjiCUVFRUmSAgMDdfLkSev6NWrU0IkTJ8w8dADAP8Tp5QAAANdx9uxZbdy4UREREQUCtyR5enoqPz9f999/v86ePauEhATFxsbqhx9+0IMPPljk/S1ZskQVKlTQrl279Oqrr2ratGmKjY2VJCUlJUmSoqOjlZaWZp0GAJRcnF4OAABwHd9//70Mw1BwcPA128TFxSk5OVnHjx+Xv7+/JGnp0qVq2LChkpKS1KpVq0Lvr0mTJpoyZYokKSgoSPPnz1dcXJzuuusueXl5Sboc9H19ff/BUQEAbhZGugEAAK6jMFfiHTlyRP7+/tbALUkNGjSQp6enjhw5UqT9NWnSxGbaz89PGRkZRdoGAKDkIHQDAABcR1BQkCwWyz++WVqZMmUKBPjc3NwC7RwdHW2mLRaL8vPz/9G+AQD2Q+gGAAC4jkqVKik8PFwLFizQhQsXCizPzMxU/fr1derUKZ06dco6/5tvvlFmZqYaNGggSfLy8lJaWprNugcOHChyPY6OjsrLyyvyegAA+yB0AwAA/I0FCxYoLy9PrVu31ooVK3T06FEdOXJE8+bNU9u2bRUWFqbGjRtrwIAB2rdvn3bv3q2BAweqQ4cOatmypSSpc+fO2rNnj5YuXaqjR49qypQpOnz4cJFrCQwMVFxcnNLT0/Xrr78W96ECAIoZoRsAAOBv1KpVS/v27VOnTp301FNPqVGjRrrrrrsUFxenhQsXymKxaM2aNapYsaLat2+vsLAw1apVSx9//LF1G+Hh4Zo8ebKeeeYZtWrVSufPn9fAgQOLXMusWbMUGxsrf39/NWvWrDgPEwBgAn6nGwAAAAAAkzDSDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmOT/AT84lwmrtwV1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "def plot_topic_frequency(df_topic_counts, top_n=50):\n",
    "    \n",
    "    top_topics = df_topic_counts.groupby('topic')['count'].sum().nlargest(top_n).reset_index()\n",
    "\n",
    "    \n",
    "    top_topics['topic'] = top_topics['topic'].str.title()\n",
    "\n",
    "    \n",
    "    top_topics['topic'] = pd.Categorical(top_topics['topic'], categories=top_topics['topic'], ordered=True)\n",
    "\n",
    "    \n",
    "    plt.figure(figsize=(10, 5))\n",
    "    ax = sns.barplot(x='count', y='topic', data=top_topics, palette='plasma')\n",
    "\n",
    "    \n",
    "    for i, row in top_topics.iterrows():\n",
    "        ax.text(row['count'] + max(top_topics['count']) * 0.01, i, f\"{row['count']:,}\", va='center')\n",
    "\n",
    "    plt.title('')\n",
    "    plt.xlabel('Count')\n",
    "    plt.ylabel('Issue Area')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_topic_frequency(df_topic_counts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
